Background Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, hotspot and other proactive policing programs aim to focus limited resources at the highest risk crime and social harm hotspots in a city. A crucial step in the implementation of these strategies is the construction of scoring models used to rank spatial hotspots. While these methods are evaluated by area normalized Recall@k (called the predictive accuracy index), models are typically trained via maximum likelihood or rules of thumb that may not prioritize model accuracy in the top k hotspots. Furthermore, current algorithms are defined on fixed grids that fail to capture risk patterns occurring in neighborhoods and on road networks with complex geometries. Results We introduce CrimeRank, a learning to rank boosting algorithm for determining a crime hotspot map that directly optimizes the percentage of crime captured by the top ranked hotspots. The method employs a floating grid combined with a greedy hotspot selection algorithm for accurately capturing spatial risk in complex geometries. We illustrate the performance using crime and traffic incident data provided by the Indianapolis Metropolitan Police Department, IED attacks in Iraq, and data from the 2017 NIJ Real-time crime forecasting challenge. Conclusion Our learning to rank strategy was the top performing solution (PAI metric) in the 2017 challenge. We show that CrimeRank achieves even greater gains when the competition rules are relaxed by removing the constraint that grid cells be a regular tessellation.

中文翻译：

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学习对时空事件热点进行排名

背景技术犯罪，交通事故，恐怖袭击和其他时空随机事件在时空上分布不均。在犯罪方面，热点和其他积极的警务计划旨在将有限的资源集中在城市中风险最高的犯罪和社会危害热点上。实施这些策略的关键步骤是构建用于对空间热点进行排名的评分模型。虽然这些方法是通过面积归一化的Recall @ k（称为预测准确性指数）进行评估的，但通常是通过最大似然法或经验法则来训练模型，而这些经验法则可能不会在前k个热点中优先考虑模型准确性。此外，当前的算法是在固定网格上定义的，这些网格无法捕获在邻域中发生的风险模式以及具有复杂几何形状的道路网络。结果我们引入了CrimeRank，用于确定犯罪热点地图的等级学习增强算法，该算法直接优化排名靠前的热点所捕获的犯罪百分比。该方法采用浮动网格结合贪婪热点选择算法，以准确捕获复杂几何形状中的空间风险。我们使用印第安纳波利斯都会警察局提供的犯罪和交通事件数据，伊拉克的IED攻击以及2017年NIJ实时犯罪预测挑战中的数据来说明性能。结论我们的学习排名策略是2017年挑战赛中表现最好的解决方案（PAI指标）。我们表明，通过消除网格单元为常规镶嵌细分的约束，放宽竞争规则后，CrimeRank可以实现更大的收益。